Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.
Typically, enabling compression on spinning disk systems
will increase speed when the content can be compressed.

## Get Started

# limitations under the License.
# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

# This generates a pure python wheel of the format "*-py3-none-any.whl"

///

## Déclarer des métadonnées

Vous pouvez déclarer les mêmes paramètres que pour `Query`.

Par exemple, pour déclarer une valeur de métadonnée `title` pour le paramètre de chemin `item_id`, vous pouvez écrire :

	switch regCnt {
	case 1:
		offset |= 0x7 << 12
	case 2:
		offset |= 0xa << 12
	case 3:
		offset |= 0x6 << 12
	case 4:
		offset |= 0x2 << 12
	default:
		return 0, errors.New("invalid register numbers in ARM64 register list")
	}
	offset |= arrangement
	// arm64 uses the 60th bit to differentiate from other archs
	// For more details, refer to: obj/arm64/list7.go
	offset |= 1 << 60
	return offset, nil

- All aggregation, conditional, type-conversion and string functions are supported.
- JSON path expressions such as `FROM S3Object[*].path` are not yet evaluated.
- Large numbers (outside of the signed 64-bit range) are not yet supported.

		//	---------- 0/0               0 2017-07-13 19:40 global1
		//	---------- 0/0               0 2017-07-13 19:40 file2
		//	gnutar: Substituting `.' for empty member name
		//	---------- 0/0               0 1969-12-31 16:00
		//	gnutar: Substituting `.' for empty member name
		//	---------- 0/0               0 2014-05-13 09:53
		//
		// According to the PAX specification, this should have been the result:

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

## How are drives used for Erasure Code?

MinIO divides the drives you provide into erasure-coding sets of *2 to 16* drives.  Therefore, the number of drives you present must be a multiple of one of these numbers.  Each object is written to a single erasure-coding set.

export MINIO_IDENTITY_LDAP_USER_DN_SEARCH_FILTER="(uid=%s)"
export MINIO_IDENTITY_LDAP_GROUP_SEARCH_BASE_DN="ou=swengg,dc=min,dc=io"
export MINIO_IDENTITY_LDAP_GROUP_SEARCH_FILTER="(&(objectclass=groupOfNames)(member=%d))"

if [ ! -f ./mc ]; then
	wget -O mc https://dl.minio.io/client/mc/release/linux-amd64/mc &&
		chmod +x mc
fi

 *
 * @version $Revision$ $Date$
 */
@SuppressWarnings("all")
public final class InputLocation implements java.io.Serializable, Cloneable, InputLocationTracker {

    // --------------------------/
    // - Class/Member Variables -/
    // --------------------------/

    /**
     * The one-based line number. The value will be non-positive if
     * unknown.
     */
    private int lineNumber = -1;

    /**